Mobile robots used in building locations often navigate based on internal maps. For example, in a multi-story building the mobile robot may have a map for each floor to aid the robot in navigating through the building. The mobile robot may, for example, use on-board sensor data to determine its location on a map through a process called “localization.”
However, localization can fail. When localization fails, it leads to a state of mislocalization in which the mobile robot cannot properly orient itself on its internal map. The localization process may fail for a variety of reasons. For example, a variety of unexpected events may create a mislocalization state. As one example, a human passerby may push a mobile robot out of an elevator onto an unexpected floor.
Conventional techniques for a robot to recover from mislocalization include a variety of manual techniques that are labor intensive. For example, onsite staff in a building can physically move a mobile robot to a known location in a building, such as moving the robot back to its docking station. However, this can be an onerous process in a large building site. Another option is to use trained operators to manually perform localization each time a robot is mislocalized. For example, a trained operator may manually relocalize a robot by comparing the robot's sensor data against known maps and through trial and error attempt to select the correct position of the robot on the map. This can be a difficult operation even for a trained operator if the sensor data of the robot is limited, there are multiple maps with similar features, or if there are identical features on a single map.